---
name: learning-needs-analysis
description: Conduct comprehensive learner audience analysis, identify performance gaps, and determine instructional requirements using surveys, interviews, and data analysis
allowed-tools: Read, Grep, Write, Edit, Glob, WebFetch
graph:
  domains: [domain:education]
  specializations: [specialization:instructional-design]
  skillAreas: [skill-area:learning-development, skill-area:user-research, skill-area:data-analysis]
  workflows: [workflow:peer-review-cycle]
  roles: [role:ux-researcher, role:business-analyst]
---

# Learning Needs Analysis

Conduct comprehensive learner audience analysis, identify performance gaps, and determine instructional requirements using surveys, interviews, and data analysis.

## Overview

This skill enables systematic analysis of learning needs to inform instructional design decisions. It encompasses audience analysis, performance gap identification, and requirements determination to ensure training and education programs address real needs effectively.

## Capabilities

### Audience Analysis
- Identify learner demographics and characteristics
- Assess prior knowledge and skills
- Determine learning preferences and styles
- Analyze motivation and attitudes
- Map learner contexts and constraints

### Performance Gap Analysis
- Define desired performance outcomes
- Assess current performance levels
- Identify gaps between current and desired states
- Distinguish training from non-training solutions
- Prioritize gaps by impact and feasibility

### Data Collection Methods
- Design and administer surveys
- Conduct stakeholder interviews
- Facilitate focus groups
- Analyze performance data
- Review existing documentation

### Requirements Documentation
- Document instructional requirements
- Define learning objectives
- Specify constraints and resources
- Identify success metrics
- Create analysis reports

## Usage Guidelines

### Analysis Process
1. Define scope and purpose of analysis
2. Identify data sources and stakeholders
3. Design data collection instruments
4. Collect and analyze data
5. Synthesize findings
6. Document recommendations

### Data Collection Best Practices
- Use multiple data sources
- Balance qualitative and quantitative methods
- Ensure representative sampling
- Maintain objectivity
- Validate findings with stakeholders

### Documentation Standards
- Clearly state methodology
- Present findings with evidence
- Distinguish facts from interpretations
- Provide actionable recommendations
- Include supporting data

## Integration Points

### Related Processes
- ADDIE Model Implementation
- Action Mapping
- Kirkpatrick Evaluation

### Collaborating Skills
- learning-objectives-writing
- assessment-item-development
- learning-analytics-interpretation

## References

- Mager and Pipe performance analysis
- Rossett needs assessment framework
- ATD competency model
- Instructional design methodologies


---
Source attribution: a5c-ai
Original project: https://github.com/a5c-ai/babysitter/blob/feb68abe397acc14f32b34984975c48fedba2b33/library/specializations/domains/social-sciences-humanities/education/skills/learning-needs-analysis/SKILL.md
License: MIT
License information: https://opensource.org/license/mit
Original upstream instructions; downloaded from Mega AI Hub.

## Upstream license notice

# MIT License

Copyright (c) 2025 A5C AI

Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.



# Mega AI Hub resource metadata

Creator / publisher: a5c-ai
Original project: https://github.com/a5c-ai/babysitter/blob/feb68abe397acc14f32b34984975c48fedba2b33/library/specializations/domains/social-sciences-humanities/education/skills/learning-needs-analysis/SKILL.md
Attribution basis: Source repository record

Fields of work: Education & Research, Human Resources
Roles: Curriculum Designer, HR Generalist, Learning & Development, People Operations, Tutor / Instructor
Specialties: Specialty not specified
Tools: Tool not specified
Country applicability: Country not specified
Classification basis: Source catalog labels
Instruction SHA-256: 2b537ad96d82ffce0294a48981f34e8010be4d0568b32b75370a140c2a5da9c5